KR20210108479A - Systems and methods for route planning - Google Patents

Abstract

The present disclosure may provide a method for route planning. The method may include obtaining a starting location and a destination of the target vehicle. The method may also include obtaining a map of the target area including the starting location and the destination. The map may include node information of each of the plurality of nodes. Further, the method may include obtaining motion state information associated with one or more vehicles different from the target vehicle in the target area. The method may include determining a target path of the target vehicle based at least in part on the departure location, the destination, motion state information associated with the one or more vehicles, and the map.

Description

Systems and methods for route planning

Cross-references to related applications

This application claims priority to Chinese Patent Application No. 201910032301.5, filed on January 14, 2019, the entire contents of which are incorporated herein by reference.

BACKGROUND This disclosure relates generally to route planning, and more particularly to systems and methods for route planning based on maps (eg, topological maps).

BACKGROUND With the development of automation technology and computer technology, route planning navigation of transport vehicles (eg, automated guided vehicles) is rapidly developing today. In general, in the case of a vehicle, the route planning system is located on a map (eg, topology map, grid map) that may include a plurality of nodes and their node information (eg, the passability of the node). It is possible to determine a route from the starting location to the destination based on the. However, in some cases, motion information of other vehicles may affect node information (eg, passability) of nodes, thus affecting the accuracy and efficiency of route planning.

Accordingly, it is desirable to provide systems and methods for accurate and efficient path planning in consideration of motion information.

In a first aspect of the present invention, a method may be provided for route planning for an Automatic Guided Vehicle (AGV) based on a topology map. The method includes: obtaining a topology map corresponding to a scheduling system; Modification by preprocessing the topology map and modifying node information of the topology map based on motion state information associated with a plurality of AGVs in the scheduling system and a plurality of nodes occupied by the plurality of AGVs in the topology map obtaining a topology map; determining a departure node and a destination node of the current AGV from the modified topology map; determining, based on node information of the modified topology map, a plurality of total costs associated with a plurality of nodes between the departure node and the destination node; and performing route planning for the current AGV based on the plurality of total costs.

In a second aspect of the invention, a device may be provided for route planning for an automatically guided vehicle (AGV) based on a topology map. The device may include at least one processor and at least one storage for storing program codes. When the program codes are executed by the at least one processor, the program codes may instruct the at least one processor to perform the method. The method includes: obtaining a topology map corresponding to a scheduling system; Modification by preprocessing the topology map and modifying node information of the topology map based on motion state information associated with a plurality of AGVs in the scheduling system and a plurality of nodes occupied by the plurality of AGVs in the topology map obtaining a topology map; determining a departure node and a destination node of the current AGV from the modified topology map; determining, based on node information of the modified topology map, a plurality of total costs associated with a plurality of nodes between the departure node and the destination node; and performing route planning for the current AGV based on the plurality of total costs.

In a third aspect of the present disclosure, a computer-readable storage medium may be provided. The computer-readable storage medium may include computer programs that, when executed by at least one processor, may perform a method. The method includes: obtaining a topology map corresponding to a scheduling system; Modification by preprocessing the topology map and modifying node information of the topology map based on motion state information associated with a plurality of AGVs in the scheduling system and a plurality of nodes occupied by the plurality of AGVs in the topology map obtaining a topology map; determining a departure node and a destination node of the current AGV from the modified topology map; determining, based on node information of the modified topology map, a plurality of total costs associated with a plurality of nodes between the departure node and the destination node; and performing route planning for the current AGV based on the plurality of total costs.

In a fourth aspect of the present disclosure, a system for route planning may be provided. The system for path planning may include at least one storage device including a set of instructions and at least one processor in communication with the at least one storage device. Upon executing the set of instructions, the at least one processor causes the system to: obtain a starting location and a destination of a target vehicle; obtain a map of a target area including the starting location and the destination, the map including node information of each of a plurality of nodes; obtain motion state information associated with one or more vehicles different from the target vehicle in the target area; determine a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with the one or more vehicles, and the map.

In a fifth aspect of the present disclosure, a method may be provided. The method may be implemented in a computing device comprising at least one processor, at least one storage medium, and a communication platform coupled to a network. The method includes: acquiring a starting location and a destination of a target vehicle; obtaining a map of a target area including the starting location and the destination, wherein the map includes node information of each of a plurality of nodes; obtaining a map of the target area; obtaining motion state information associated with one or more vehicles different from the target vehicle in the target area; and determining a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with one or more vehicles, and the map.

In a sixth aspect of the present disclosure, a system for route planning may be provided. The system includes a location obtaining module configured to obtain a starting location and a destination of the target vehicle; a map acquiring module configured to acquire a map of a target area including the departure location and the destination, wherein the map includes node information of each of a plurality of nodes; a motion status obtaining module configured to obtain motion status information associated with one or more vehicles different from the target vehicle in the target area; and a route determination module configured to determine a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with one or more vehicles, and the map.

In a seventh aspect of the present disclosure, a non-transitory computer-readable medium may be provided. The non-transitory computer-readable medium may include executable instructions that, when executed by the at least one processor, instruct the at least one processor to perform the method. The method includes: acquiring a starting location and a destination of a target vehicle; obtaining a map of a target area including the starting location and the destination, wherein the map includes node information of each of a plurality of nodes; obtaining a map of the target area; obtaining motion state information associated with one or more vehicles different from the target vehicle in the target area; and determining a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with one or more vehicles, and the map.

Additional features will be set forth in part in the description that follows, and in part will be apparent to those skilled in the art upon examination of the following and accompanying drawings, or may be learned by making or operating examples. Features of the present disclosure may be realized and attained by the practice or use of various aspects of the methods, means and combinations set forth in the detailed examples discussed below.

The present disclosure is further described with respect to exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. The drawings are not to scale. These embodiments are non-limiting schematic embodiments, wherein like reference numerals indicate like structures throughout the various parts of the drawings:
1 is a schematic diagram illustrating an exemplary route planning system in accordance with some embodiments of the present disclosure.
2 is a schematic diagram illustrating typical hardware and/or software components of a typical computing device in accordance with some embodiments of the present disclosure.
3 is a schematic diagram illustrating typical hardware and/or software components of a typical mobile device in accordance with some embodiments of the present disclosure.
4 is a flowchart illustrating an exemplary process for route planning for an automatically guided vehicle (AGV) based on a topology map in accordance with some embodiments of the present disclosure.
5 is a flowchart illustrating an exemplary process for route planning for an AGV in accordance with some embodiments of the present disclosure.
6 is a block diagram illustrating an exemplary processing device in accordance with some embodiments of the present disclosure;
7 is a flowchart illustrating an exemplary process for determining a target path of a target vehicle in accordance with some embodiments of the present disclosure.
8 is a flowchart illustrating an exemplary process for determining a target path of a target vehicle in accordance with some embodiments of the present disclosure.
9 is a flow diagram illustrating an exemplary process for allocating a task in accordance with some embodiments of the present disclosure.
10 is a schematic diagram illustrating typical node information of a node according to some embodiments of the present disclosure.
11 is a schematic diagram illustrating exemplary nodes associated with a stationary vehicle and exemplary nodes associated with a moving stationary vehicle in accordance with some embodiments of the present disclosure.

In the following detailed description, numerous specific details are set forth as examples in order to provide a thorough understanding of the related disclosure. However, it should be apparent to those skilled in the art that the present disclosure may be practiced without these details. In other instances, well-known methods, procedures, systems, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present disclosure. became Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the disclosure. Accordingly, the present disclosure is not limited to the illustrated embodiments, but is to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. As used herein, singular forms "a", "an", and "the" may also be intended to include plural forms, unless the context clearly dictates otherwise. The terms "comprises," and/or "comprising," "includes," and/or "comprising," as used herein, refer to the described features, integers, steps, and operations. , elements, and/or elements, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, elements, and/or groups thereof. It will also be understood that no

The terms "system," "unit," "module," and/or "block", as used herein, are used herein to distinguish between different components, elements, parts, sections, or assemblies at different levels in ascending order. It will be understood that this is one way for However, terms may be replaced by another expression if they serve the same purpose.

The modules (or units, blocks, units) described in this disclosure may be implemented as software and/or hardware modules, and stored in any tangible non-transitory computer-readable medium or other storage devices. can be In some embodiments, a software module may be linked into a compiled and executable program. It will be appreciated that software modules may be callable from other modules or themselves, and/or may be invoked in response to detected events or interrupts. Software modules configured for execution on computing devices may be stored on a computer-readable medium, such as a compact disk, digital video disk, flash drive, magnetic disk, or any other type of medium, or as a digital download (and prior to execution). (which may be originally stored in a compressed or installable format) requiring installation, decompression, or decryption in Such software code may be partially or fully stored in a memory device of the executing computing device for execution by the computing device. Software instructions may be included in firmware, such as an EPROM. Hardware modules (eg, circuits) may be included in connected or coupled logic units, such as gates and flip-flops, and/or programmable, such as programmable gate arrays or processors. It will also be understood that units may be included. The modules or computing device functionality described herein are preferably implemented in hardware modules, but may also be software modules. In general, the modules described herein are referred to as logical modules that can be combined with other modules or divided into units regardless of their physical configuration or storage.

When a unit, engine, module, or block is referred to as being “on,” “connected to,” or “coupled to” another unit, engine, module, or block, it is another unit, engine, module, or block. There may be intermediary units, engines, modules, or blocks that are directly on, connected, or combined on a module, or block, or unless the context clearly dictates otherwise. As used herein, the term (“and/or”) includes any and all combinations of one or more of the associated listed items.

These and other features, and characteristics of the present disclosure, as well as methods of operation and functions of combinations of related elements and parts of the structure, and economics of manufacture, all form part of the appended It will become more apparent upon consideration of the following description with reference to the drawings. However, it will be clearly understood that the drawings are for purposes of illustration and description only, and are not intended to limit the scope of the present disclosure.

The flowcharts used in this disclosure illustrate operations in which systems are implemented in accordance with some embodiments of the present disclosure. It should be clearly understood that the operations in the flowcharts may not be implemented in order. Conversely, the operations may be implemented in reverse order or concurrently. Also, one or more other operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.

One aspect of the present disclosure relates to systems and methods for route planning based on a map (eg, a topology map). The map includes a plurality of nodes and node information therefor, for example, the location of the node, the barcode of the node, the coordinates of the node, the passability of the node, the turn capability of the node, the number of neighboring nodes, the neighboring nodes It may include the connectivity of the node to . The systems may determine the starting location and destination of the target vehicle. The systems may also obtain motion state information associated with one or more vehicles other than the target vehicle located in the target area where the target vehicle is located. The systems may generate the target map by modifying at least a portion of the node information of each of at least one of the plurality of nodes based on the motion state information associated with the one or more vehicles. The systems may then determine a target route of the target vehicle based on the target map, the starting location, and the destination. When determining the target route, the systems may determine a plurality of costs associated with the plurality of target nodes between the departure node corresponding to the departure location and the destination node corresponding to the destination.

According to the systems and methods of the present disclosure, motion state information associated with other vehicles may be taken into account so that the target vehicle does not collide with other vehicles as it moves along a target path. In addition, when determining a target path, the cost of a node depends on various parameters such as a weight value of a node, a parent node cost of a node, a distance cost from a parent node to the node, a turn cost from a parent node to the node, and the like. , which can improve the accuracy and rationality of the target path.

1 is a schematic diagram illustrating an exemplary route planning system in accordance with some embodiments of the present disclosure. In some embodiments, the route planning system (also referred to as a “scheduling system”) 100 includes a server 110 , a network 120 , one or more vehicles 130 , one or more terminal devices 140 , and A storage device 150 may be included. In some embodiments, the route planning system 100 may be applied to various scenarios, for example, AGV (Automated Guided Vehicle) based freight transportation, tourism in a predetermined area (eg, a park), automatic food delivery, etc. can

Server 110 may be a single server or a group of servers. The server group may be centralized or distributed (eg, server 110 may be a distributed system). In some embodiments, server 110 may be local or remote. For example, server 110 may access information and/or data stored in vehicle(s) 130 , terminal device(s) 140 and/or storage device 150 via network 120 . have. As another example, the server 110 may be directly connected to the vehicle(s) 130 , the terminal device(s) 140 , and/or the storage device 150 to access stored information and/or data. In some embodiments, server 110 may be implemented on a cloud platform or on an onboard computer. By way of example only, a cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, cross-cloud, multi-cloud, etc., or any combination thereof. In some embodiments, server 110 may be implemented on computing device 200 including one or more components illustrated in FIG. 2 of this disclosure.

In some embodiments, server 110 may include processing device 112 . The processing device 112 may process information and/or data associated with route planning and/or task assignment to perform one or more functions described in this disclosure. For example, the processing device 112 may obtain motion state information associated with one or more vehicles different from the target vehicle in the target area. The processing device 112 may also determine a target path of the target vehicle based at least in part on a starting location of the target vehicle, a destination of the target vehicle, motion state information associated with the one or more vehicles, and a map of the target area. In some embodiments, processing device 112 may include one or more processing engines (eg, single core processing engine(s) or multi-core processor(s)). By way of example only, processing device 112 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor ( DSP), Field-Programmable Gate Array (FPGA), Programmable Logic Device (PLD), Controller, Microcontroller Unit, Reduced Instruction-Set Computer (RISC) ) , a microprocessor, etc., or any combination thereof.

In some embodiments, server 110 may include one or more components (eg, vehicle(s) 130 , terminal device(s) 140 , storage device 150 ) of route planning system 100 . ) may be connected to the network 120 to communicate with the In some embodiments, server 110 may include one or more components (eg, vehicle(s) 130 , terminal device(s) 140 , storage device 150 ) of route planning system 100 . ) can be connected directly to or communicate with them.

Network 120 may facilitate the exchange of information and/or data. In some embodiments, one or more components of route planning system 100 (eg, server 110 , vehicle(s) 130 , terminal device(s) 140 , storage device 150 ) ) may transmit information and/or data to other component(s) of route planning system 100 via network 120 . For example, the server 110 may obtain a map of the target area from the storage device 150 through the network 120 . In some embodiments, network 120 may be any type of wired or wireless network, or a combination thereof. By way of example only, network 120 may include a cable network, a wireline network, a fiber optic network, a telecommunications network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), and a wireless local area network. (WLAN: Wireless Local Area Network), Metropolitan Area Network (MAN: Metropolitan Area Network), Wide Area Network (WAN), Public Telephone Switched Network (PSTN), Bluetooth Network, ZigBee Network, Near Field a Near Field Communication (NFC) network, or the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired or wireless network access points (eg, point 120-1, point 120-2), through which one or more components of route planning system 100 . may be connected to the network 120 to exchange data and/or information.

The vehicle(s) 130 may include a plurality of vehicles 130-1, 130-2, ..., 130-n. Vehicle(s) 130 may include autonomously guided vehicles (AGVs), taxis, private cars, hitches, buses, trains, bullet trains, high-speed rail, subways, watercraft, aircraft, spacecraft, hot air balloons, unmanned vehicles (eg, , unmanned aerial vehicles, unmanned vehicles), etc., or any combination thereof. Vehicle(s) 130 may communicate with server 110 , terminal device(s) 140 and/or storage device 150 via network 120 . In some embodiments, vehicle(s) 130 may receive a task assigned by server 110 (eg, deliver goods) and perform the task along a route determined by server 110 . have. In some embodiments, the vehicle(s) 130 may transmit motion state information of the vehicle(s) 130 to the server 110 via the network 120 , and the server 110 may send the vehicle(s) When determining the path of the motion state information can be processed.

Terminal device 140 may be configured to receive information and/or data from server 110 , vehicle(s) 130 , and/or storage device 150 via network 120 . For example, the terminal device 140 may provide information (eg, delivery of goods) associated with the task (eg, delivery of goods) from the server 110 and/or the vehicle that is trying to perform or is performing the task via the network 120 . route, departure location, destination). In some embodiments, the terminal device 140 may provide a user interface through which the user may view information and/or input data and/or commands for the route planning system 100 . For example, a user may view information (eg, route, departure location, destination) associated with a task through the user interface. As another example, the terminal device 140 may input a command through the user interface and transmit the command to the vehicle(s) 130 and/or the server 110 via the network 120 . The command may be a command to execute a task, a command to stop executing the task, a command to set or modify a parameter associated with the task, a command to charge one of the vehicle(s) 130 , a vehicle(s) 130 command ), commands to maintain one of the vehicle(s) 130 , commands to assign a task to one of the vehicle(s) 130 , and the like.

In some embodiments, terminal device(s) 140 include mobile device 140 - 1 , tablet computer 140 - 2 , laptop computer 140 - 3 , device 140 - embedded in vehicle 130 . 4), the wearable device 140-5, or the like, or any combination thereof. In some embodiments, mobile device 140 - 1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof. A smart home device may include a smart lighting device, a control device of an intelligent electric device, a smart monitoring device, a smart television, a smart video camera, an intercom, etc., or any combination thereof. The wearable device may include a smart bracelet, smart footgear, smart glasses, smart helmet, smart watch, smart clothing, smart backpack, smart accessory, or the like, or any combination thereof. A smart mobile device may include a smartphone, a personal digital assistant (PDA), a gaming device, a navigation device, a point of sale (POS) device, etc., or any combination thereof. The virtual reality device and/or augmented reality device may include a virtual reality helmet, virtual reality glasses, virtual reality patch, augmented reality helmet, augmented reality glasses, augmented reality patch, etc., or any combination thereof. For example, virtual reality devices and/or augmented reality devices can be^TM This can include Glass, Oculus Rift, HoloLens, Gear VR, and more. In some embodiments, the embedded device 140 - 4 may include an onboard computer, an onboard television, or the like.

In some embodiments, terminal device 140 includes a display capable of presenting information in a human readable form, such as text, image, audio, video, graph, animation, etc., or any combination thereof. can do. The display of the terminal device 140 includes a cathode ray tube (CRT) display, a liquid crystal display (LCD), a light emitting diode (LED) display, a plasma display panel (PDP), a three-dimensional (3D) display, or the like, or a display thereof. Combinations may be included. In some embodiments, the terminal device 140 may be connected to one or more components of the route planning system 100 (eg, the server 110 , the vehicle(s) 130 , the storage device via the network 120 ). (150)) can be connected. In some embodiments, server 110 may be integrated into terminal device(s) 140 .

Storage device 150 may store data and/or instructions. In some embodiments, the storage device 150 may store data obtained from the server 110 , the vehicle(s) 130 , the terminal device(s) 140 , an external storage device, and the like. For example, the storage device 150 may store a map of the target area. As another example, the storage device 150 may store a target route of the target vehicle. In some embodiments, storage device 150 may store data and/or instructions that server 110 may execute or use to perform the exemplary methods described in this disclosure. For example, the storage device 150 may store instructions that the route planning system 100 may execute or use to obtain motion state information associated with one or more vehicles different from the target vehicle in the target area. As another example, the storage device 150 may be configured such that the route planning system 100 determines the target vehicle's target based at least in part on the starting location of the target vehicle, the destination of the target vehicle, motion state information associated with one or more vehicles, and the map. You can store commands that can be executed or used to determine a path.

In some embodiments, storage device 150 may include mass storage, removable storage, volatile read-and-write memory, read-only memory (ROM), etc., or any combination thereof. Typical mass storage may include magnetic disks, optical disks, solid-state drives, and the like. Typical removable storage may include flash drives, floppy disks, optical disks, memory cards, zip disks, magnetic tape, and the like. A typical volatile read-and-write memory may include random access memory (RAM). Typical RAMs may include dynamic RAM (DRAM), dual data rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and zero-capacitor RAM (Z-RAM), and the like. . Typical ROMs are masked ROM (MROM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), compact disk ROM (CD-ROM), and digital general purpose disk ROM. and the like. In some embodiments, storage device 150 may be implemented on a cloud platform. By way of example only, a cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, cross-cloud, multi-cloud, etc., or any combination thereof.

In some embodiments, storage device 150 is one or more components of route planning system 100 (eg, server 110 , vehicle(s) 130 , terminal device(s) 140 ) ) may be connected to the network 120 to communicate with the One or more components of route planning system 100 may access data or instructions stored in storage device 150 via network 120 . In some embodiments, storage device 150 is directly connected to one or more components of route planning system 100 (server 110 , vehicle(s) 130 , terminal device(s) 140 ). or communicate. In some embodiments, storage device 150 may be part of server 110 . For example, the storage device 150 may be integrated into the server 110 .

It should be noted that the route planning system 100 is provided for illustrative purposes only and is not intended to limit the scope of the present disclosure. Numerous variations and modifications may be made to those skilled in the art under the teachings of this disclosure. However, these variations and modifications do not depart from the scope of the present disclosure.

2 is a schematic diagram illustrating typical hardware and/or software components of a typical computing device in accordance with some embodiments of the present disclosure. In some embodiments, server 110 may be implemented on computing device 200 . For example, processing device 112 may be implemented on computing device 200 and configured to perform functions of processing device 112 disclosed in this disclosure.

Computing device 200 may be used to implement any of the components of route planning system 100 described herein. For example, processing device 112 may be implemented on computing device 200 via hardware, a software program, firmware, or a combination thereof. Although only one such computer is shown for convenience, the computer functions associated with route planning and/or scheduling described herein may be implemented in a distributed fashion on many similar platforms to distribute the processing load.

For example, computing device 200 may include COM ports 250 coupled to and from a network coupled thereto to facilitate data communication. Computing device 200 may also include a processor (eg, processor 220 ) in the form of one or more processors (eg, logic circuits) for executing program instructions. For example, the processor 220 may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from the bus 210 , wherein the electronic signals encode structured data and/or instructions for processing by the processing circuits. The processing circuits may perform logical calculations and then determine a conclusion, result, and/or instruction encoded into electronic signals. The interface circuits may then send electronic signals from the processing circuit via bus 210 .

Computing device 200 may also include one or more repositories configured to store various data files (eg, program instructions) to be processed and/or transmitted by computing device 200 . In some embodiments, the one or more storages include high-speed random access memory (not shown), non-volatile memory (eg, magnetic storage device, flash memory, or other non-volatile solid-state memory) (not shown), disk 270 , read only memory (ROM) 230 , or random access memory (RAM) 240 , or the like, or any combination thereof. In some embodiments, the one or more storages may further include a remote storage corresponding to the processor 220 . The remote storage may be coupled to the computing device 200 via the network 120 . Computing device 200 also includes program instructions stored in one or more storages (eg, ROM 230 , RAM 240 , and/or other types of non-transitory storage media) to be executed by processor 220 . can do. The methods and/or processes of the present disclosure may be implemented as program instructions. Computing device 200 may also include an I/O component 260 that supports input/output between computing device 200 and other components. Computing device 200 may also receive programming and data via network communications.

For illustrative purposes only, only one processor is shown in FIG. 2 . Multiple processors 220 are also implemented; Accordingly, the operations and/or method steps performed by one processor 220 as described in this disclosure may also be performed jointly or separately by multiple processors. For example, if in this disclosure the processor 220 of the computing device 200 executes both operations A and B, then operations A and B also perform two different processors 220 in the computing device 200 . (e.g., a first processor executes operation A and a second processor executes operation B, or first and second processors execute operations A and B jointly).

It should be noted that the above description is provided for purposes of illustration only, and is not intended to limit the scope of the present disclosure. Numerous variations and modifications may be made to those of ordinary skill in the art under the teachings of this disclosure. However, these variations and modifications do not depart from the scope of the present disclosure.

3 is a schematic diagram illustrating typical hardware and/or software components of a typical mobile device in accordance with some embodiments of the present disclosure. In some embodiments, server 110 (eg, processing device 112 ) or terminal device(s) 140 may be implemented on mobile device 300 .

As illustrated in FIG. 3 , the mobile device 300 includes a communication platform 310 , a display 320 , a graphics processing unit (GPU) 330 , a central processing unit (CPU) 340 , and an I/O 350 . ), memory 360 , mobile operating system (OS) 370 , and storage 390 . In some embodiments, any other suitable components may also be within mobile device 300 , including but not limited to a system bus or controller (not shown).

In some embodiments, mobile operating system 370 (eg, iOS™, Android™, Windows Phone™, etc.) and one or more applications 380 are from storage 390 to be executed by CPU 340 . It may be loaded into the memory 360 . Applications 380 may include a browser or any other suitable mobile apps for receiving and rendering information regarding behavior recognition or other information from route planning system 100 . User interactions with the information stream may be accomplished via I/O 350 and provided to processing device 112 and/or other components of route planning system 100 via network 120 .

4 is a flowchart illustrating an exemplary process for route planning for an automatically guided vehicle (AGV) based on a topology map in accordance with some embodiments of the present disclosure. In some embodiments, process 400 may be implemented as a set of instructions (eg, an application) stored in storage ROM 230 or RAM 240 . The processor 220 and/or modules of FIG. 6 may execute a set of instructions, and when executing the instructions, the processor 220 and/or modules may be configured to perform the process 400 . The operations of the illustrated process provided below are intended to be exemplary. In some embodiments, process 400 may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order of the operations of the process as illustrated in FIG. 4 and described below is not intended to be limiting.

At 401 , a topology map corresponding to the scheduling system (eg, route planning system 100 ) may be obtained. The topology map may be obtained by the processing device 112 (eg, the location obtaining module 610 or the map obtaining module 620 shown in FIG. 6 ). In the present specification, the topology map may be a map in which an environment (eg, an indoor environment) may be represented by a plurality of nodes and connection relationships between the plurality of nodes. In some embodiments, the topology map may include the location of each of the plurality of nodes and node information therefor.

At 402 , the topology map may be preprocessed, and a modified topology map of the topology map is generated based on motion state information associated with the plurality of AGVs in the scheduling system and the plurality of nodes occupied by the plurality of AGVs in the topology map. It can be obtained by modifying node information. The motion state information associated with the plurality of AGVs may be obtained by the processing device 112 (eg, the motion state obtaining module 630 shown in FIG. 6 ), and the topology map may be obtained by the processing device 112 (eg, For example, it may be pre-processed by the path determination module 640 shown in FIG. 6 .

At 403 , a departure node and a destination node of the current AGV in the modified topology map may be determined. A plurality of total costs (also referred to as “costs” for brevity) associated with the plurality of nodes between the departure node and the destination node may be determined based on node information in the modified topology map. Route planning for the current AGV may be performed based on the plurality of total costs. Operation 403 may be performed by processing device 112 (eg, path determination module 640 shown in FIG. 6 ).

In some embodiments, the topology map may include a global environment topology map or a local environment topology map stored in the scheduling system. For example, the global environment topology map may be a map in which the entire indoor environment may be reflected, and may be a map that may include node information indicating the entire indoor environment; The local environment topology map may be a map including a specific region from a departure node to a destination node. In some embodiments, the topology map may also be obtained by the scheduling system from another storage device that stores the topology maps.

In some embodiments, for each of the plurality of nodes in the topology map, the location of the node is mapped to a serial number or barcode (eg, a one-dimensional barcode, a two-dimensional barcode (eg, a quick response (QR) code)). The node information of a node is the weight value of the node, the passability of the node (for example, whether a vehicle can pass through the node), and the turning ability of the node (for example, the number of neighboring nodes), the number of neighboring nodes (eg, 4, 8), the node's connectivity to neighboring nodes, etc., or any combination thereof, in some embodiments, the weight of the node. The higher the value, the higher the total cost of the node, and thus the higher the probability that the vehicle will bypass the node.

In some embodiments, the scheduling system may obtain locations of initial nodes (eg, departure nodes) occupied by the plurality of AGVs and locations of destination nodes of tasks executed by the plurality of AGVs. can The scheduling system may also obtain motion state information associated with a plurality of AGVs. In some embodiments, the plurality of AGVs may include one or more stationary AGVs and one or more moving AGVs. Thus, the shortest path from the origin node to the destination node bypassing other AGVs can be planned for the current AGV based on the modified topology map.

In some embodiments, the scheduling system may also obtain states of a plurality of AGVs. For a particular AGV, states can include idle states (also referred to as “schedulable states”) and non-idle states (also referred to as “non-schedulable states”). Idle state may refer to no tasks to be executed (or running) by a particular AGV; The non-idle state may refer to that there is a task to be executed (or running) by a particular AGV. Additionally or alternatively, the non-idle state may also refer to that a particular AGV may be in charge, in maintenance, and the like. In some embodiments, according to states of the plurality of AGVs, a task may be assigned to an AGV that is in an idle state. As used herein, a task may include a designated task corresponding to a designated AGV and an unassigned task corresponding to an unassigned AGV (eg, any AGV in an idle state).

According to different AGVs corresponding to different tasks, a task may be assigned to an AGV in the following two situations.

Situation 1: When the designated AGV corresponding to the designated task is idle in the modified topology map, the designated task may be assigned to the designated AGV; If the designated AGV is in the non-idle state, the designated task may be assigned to the designated AGV after the AGV becomes idle.

Situation 2: For each of each unassigned task, the idle unassigned AGV closest to the destination node of the unassigned task may be determined from the modified topology map, and the unassigned task may be assigned to the unassigned AGV.

In some embodiments, the (modified) topology map may also be updated based on changes in node information of the (modified) topology map. In some embodiments, some of the node information may be updated. Additionally or alternatively, all node information may be updated. For example, the node's weight value, the node's passability, and/or the node's turn ability may be updated. The way to update the (modified) topology map can be defined by the user. For example, the (modified) topology map may be updated at regular time intervals (eg, every minute, every two minutes, every five minutes, monthly, every two months).

In some embodiments, the pre-processing of the topology map may include a first pre-processing and a second pre-processing. In some embodiments, the topology map may be preprocessed by separately performing the first preprocessing or the second preprocessing. In some embodiments, the topology map may be preprocessed by collectively performing the first preprocessing and the second preprocessing. For purposes of illustration, a situation in which a topology map is preprocessed by first performing a first preprocessing and then performing a second preprocessing is described as an example.

The first preprocessing may be performed on the topology map by modifying node information corresponding to one or more stopped AGVs, for example, a weight value of a node, passability of a node, turn ability of a node, and the like. Also, a first route planning may be performed for the current AGV based on the first modified topology map. A current AGV collides with at least one of the one or more moving AGVs when it is detected that a first path in the first path plan at least partially overlaps with at least a portion of the one or more paths corresponding to the one or more moving AGVs. can be determined to be Then, secondary preprocessing may be performed on the topology map. In addition, the second route planning may be performed for the current AGV based on the second modified topology map using the node in which the current AGV is located as the second starting node.

In some embodiments, the scheduling system may determine the starting node of the current AGV based on the nodes of the (modified) topology map. If the current AGV is not located on the node, the node closest to the current AGV may be designated as the departure node of the current AGV for route planning. If the closest node is occupied by other AGVs, the next closest node to the current AGV may be selected as the starting node for route planning.

In some embodiments, node information of a plurality of nodes of the modified topology map (eg, a weight value of a node, passability of a node, turn ability of a node, number of neighboring nodes, connectivity of a node to neighboring nodes) ) can be determined. Also, in the modified topology map, a plurality of nodes to which the current AGV may move may be determined based on node information, and a plurality of total costs associated with the plurality of nodes may be determined. Route planning for the current AGV may then be performed based on the plurality of total costs.

In some embodiments, the process for determining the total cost associated with a node between the originating node and the destination node may be an iterative process comprising a plurality of cycles. For each cycle, an updated current node may be determined, and at least one next node of the updated current node may be determined. One of the at least one next node (eg, the next node with the minimum total cost) may be determined as the next updated current node based on the at least one total cost associated with the at least one next node.

According to embodiments of the present disclosure, the topology map obtained by the scheduling system is an updated topology map that may reflect changes in node information of the topology map in actual situations. In addition, preprocessing on the topology map can ensure that the nodes of the planned path are reachable and that the planned path is consistent with real situations. The first preprocessing performed on the topology map may ensure that the current AGV traveling along the first path of the first path plan avoids other stationary AGVs; The second preprocessing performed on the topology map may ensure that the current AGV moving along the second path of the second plan avoids one or more moving AGVs. Therefore, according to embodiments of the present disclosure, a situation in which the current AGV may collide with other AGVs can be avoided.

In some embodiments, the pre-processing for the topology map may include a first pre-processing and/or a second pre-processing.

According to the first preprocessing performed in the topology map, for each of the one or more stationary AGVs different from the current AGV in the topology map, the node information of one or more nodes within a preset area centered on the stationary AGV may be modified. . Also, a first modified topology map may be generated based on the modified node information.

According to the second preprocessing performed in the topology map, for each of the one or more stationary AGVs different from the current AGV in the topology map, the node information of one or more nodes within a preset area centered on the stationary AGV may be modified, ; For each of the one or more moving AGVs in the topology map, node information of one or more nodes of the planned path not passed by the moving AGV may be modified. In addition, a second modified topology map may be generated based on the modified node information.

In some embodiments, for each of the one or more stationary AGVs different from the current AGV in the topology map, one or more nodes within the preset area centered on the stationary AGV are located within the area determined by expanding the stationary AGV by a specified size. It may contain nodes. The specified size may be a default setting of the route planning system 100 or may be adjusted according to different circumstances. For example, the designated size may be determined based on the size of the stationary AGV and/or the distance between neighboring nodes in the topology map.

In some embodiments, the first pre-treatment and the second pre-treatment may be performed separately.

For example, the first preprocessing may be performed in the topology map by modifying node information corresponding to one or more stopped AGVs. Accordingly, nodes along the planned path (eg, the first path) for the current AGV may be determined based on node information of the nodes of the first modified topology map.

As another example, the second preprocessing may be performed in the topology map by modifying node information corresponding to one or more stationary AGVs and node information corresponding to one or more moving AGVs. Accordingly, the nodes along the planned path (eg, the second path) for the current AGV may be determined based on node information of the nodes of the second modified topology map.

In some embodiments, the first pre-processing and the second pre-processing may be performed in batches.

First, a first modified topology map may be obtained by performing a first pre-processing on the topology map.

Second, a first route planning may be performed for the current AGV based on the first modified topology map. If the current AGV is detected to collide with at least one of the one or more moving AGVs when moving along the first path of the first path plan, it may be determined that the first path plan has failed. Then, secondary preprocessing may be performed on the topology map.

In some embodiments, a departure node and a destination node of the current AGV may be determined in the modified topology map. According to the node information of the modified topology map, a plurality of total costs associated with a plurality of nodes between the departure node and the destination node may be determined. Also, route planning for the current AGV may be performed based on the plurality of total costs.

Specifically, the first departure node and the first destination node of the current AGV may be determined from the first modified topology map. A plurality of first total costs associated with the plurality of nodes between the first departure node and the first destination node may be determined based on node information of the first modified topology map. A first route planning for the current AGV may be performed based on the plurality of first total costs.

In some embodiments, the first starting node may be the first available node closest to the starting location of the current AGV (eg, the location of the current AGV) in the first modified topology map. The first destination node may be the first available node closest to the destination of the current AGV in the first modified topology map. In this specification, an available node refers to a node that is not a barrier as a node that is not occupied by other AGVs.

In response to detecting that the current AGV collides with at least one of the one or more moving AGVs when the current AGV moves along the first path of the first path plan, a second of the current AGV in the second modified topology map A departure node and a second destination node may be determined. A plurality of second total costs associated with the plurality of nodes between the second departure node and the second destination node may also be determined based on the node information of the second modified topology map. Also, a second route planning for the current AGV may be performed based on the plurality of second total costs.

In some embodiments, the second starting node may be the second available node closest to the starting location of the current AGV (eg, the location of the current AGV) in the second modified topology map. The second destination node may be a second available node closest to the destination of the current AGV in the second modified topology map. In some embodiments, the second starting node may be the same as or different from the first starting node; Similarly, the second destination node may be the same as or different from the first destination node. For example, if the first available node is not a barrier in the second modified topology map or is not occupied by other AGVs, the second starting node may be the same as the first starting node.

In some embodiments, the total cost corresponding to the node may include a first cost from the starting node to the corresponding node and a second cost from the corresponding node to the destination node.

In some embodiments, for each of the one or more stationary AGVs different from the current AGV in the topology map, the node information of one or more nodes within a preset area centered on the stationary AGV includes one or more weight values of the one or more nodes. It may be modified by modifying to one or more high weight values (eg, weight values greater than a preset threshold) and setting one or more nodes to non-turnable nodes. Taking a specific node as an example, a high weight value may indicate that the AGV can bypass the specific node.

In some embodiments, for each of the stationary AGVs different from the current AGV in the topology map, the node information of one or more nodes within a preset area centered on the stationary AGV is to nodes that cannot pass through the one or more nodes. It can be modified by setting and designating one or more nodes as one or more barriers. For each of the one or more moving AGVs in the topology map, the node information of one or more nodes of the planned path not traversed by the moving AGV may include the one or more weight values of the one or more nodes to one or more high weight values (e.g., in advance). It can be modified by setting weight values greater than the set threshold value).

In some embodiments, after the departure node and the destination node of the current AGV are determined in the modified topology map, if it is determined that the departure node or the destination node is a barrier based on the node information of the modified topology map, it is determined that the route planning has failed. can be decided. In some embodiments, if it is determined that the starting node is a barrier, the next closest node to the current AGV (assuming that node is not a barrier and is not occupied by other AGVs) may be designated as the new starting node. and new route planning can be performed for the current AGV.

In some embodiments, the originating node may be designated as the first current node of the route. For each cycle, an updated current node is determined, and if the updated current node is not the destination node, a next node of the updated current node may be determined. Specifically, one or more total costs associated with one or more nodes other than those of the path may be determined, and the node with the minimum total cost may be determined as a node on the path and a next node of the updated current node. Also, the next node may be determined as the updated current node in the next cycle.

In some embodiments, the total cost corresponding to that node is the cost of the path from the parent node (which may be considered the "current node") to that node (which may be considered the "next node"), e.g. For example, it may include a plurality of parameters reflecting a cost from a parent node to a corresponding node, a turn cost from a parent node to a corresponding node, a weight value of the corresponding node, or any combination thereof. It should be noted that typical parameters are provided for illustrative purposes and other parameters may also be used according to actual needs. For example, the parameters may also include price information (eg toll gates, fuel consumption), road information (eg road type, road width, traffic lights, traffic control, road congestion, speed limits), etc., or their It can include parameters for any combination.

In some embodiments, the total cost corresponding to the node may be determined based on the cost from the current node to the node and/or the cost from the node to the destination node.

For example, the total cost corresponding to the node may be determined according to Equation (1) below:

(1)

(One)

은 해당 노드의 일련 번호를 나타내고,

은 해당 노드에 대응하는 총 비용을 나타내고,

은 현재 노드로부터 해당 노드까지의 비용을 나타내고,

은 해당 노드로부터 목적지 노드까지의 비용을 나타낸다.here

represents the serial number of the node,

represents the total cost corresponding to the node,

represents the cost from the current node to the corresponding node,

represents the cost from the node to the destination node.

In some embodiments, the cost from that node to the destination node may be determined using the Manhattan distance according to Equation (2) below:

(2)

(2)

및

는 목적지 노드의 X 좌표 및 Y 좌표를 각각 나타내고,

및

은 해당 노드의 X 좌표 및 Y 좌표를 각각 나타낸다.here

and

represents the X coordinate and Y coordinate of the destination node, respectively,

and

represents the X coordinate and Y coordinate of the corresponding node, respectively.

In some embodiments, the cost from the current node to the corresponding node is a total cost corresponding to the current node, a weight value of the node, a distance cost determined based on the distance from the current node to the corresponding node, and/or from the current node It may be determined based on the turn cost to the corresponding node.

For example, the cost from the current node to the corresponding node may be determined according to Equation (3) below:

(3)

(3)

은 해당 노드의 가중치 값을 나타내고,

는 현재 노드에 대응하는 총 비용을 나타내고,

은 현재 노드로부터 해당 노드까지의 거리에 따라 결정된 거리 비용을 나타내고,

은 현재 노드로부터 해당 노드로의 턴 비용을 나타낸다.here

represents the weight value of the node,

represents the total cost corresponding to the current node,

represents the distance cost determined according to the distance from the current node to the corresponding node,

represents the turn cost from the current node to the corresponding node.

In some embodiments, the cost of the distance from the current node to that node may be determined according to Equation (4) below:

(4)

(4)

및

는 현재 노드의 X 좌표 및 Y 좌표를 각각 나타내고,

및

은 해당 노드의 X 좌표 및 Y 좌표를 각각 나타낸다.here

and

represents the X and Y coordinates of the current node, respectively,

and

represents the X coordinate and Y coordinate of the corresponding node, respectively.

In some embodiments, the turn cost of that node from the current node may be determined according to Equation (5) below:

(5)

(5)

는 현재 노드의 타겟 각도(target angle)를 나타내고,

은 해당 노드의 타겟 각도를 나타내고,

는 턴 비용의 가중 인자를 나타낸다.here

represents the target angle of the current node,

represents the target angle of the node,

denotes the weighting factor of the turn cost.

In some embodiments, the node with the least cost may be determined based on a binary heap structure.

In some embodiments, the starting node of the current AGV may be determined in the modified topology map. Specifically, the node closest to the current AGV may be determined. A corresponding node may be designated as a starting node when the corresponding node is not occupied by AGVs different from the current AGV.

In some embodiments, the node information of the topology map may include the node's weight value, the node's passability (eg, whether a vehicle can pass through the node), and the node's turn ability (eg, the vehicle's ability to pass through the node). can be rotated), the number of neighboring nodes (eg, 4, 8), the node's connectivity to neighboring nodes, etc., or any combination thereof.

5 is a flowchart illustrating an exemplary process for route planning for an AGV in accordance with some embodiments of the present disclosure. In some embodiments, process 500 may be implemented as a set of instructions (eg, an application) stored in storage ROM 230 or RAM 240 . The processor 220 and/or modules of FIG. 6 may execute a set of instructions, and when executing the instructions, the processor 220 and/or modules may be configured to perform the process 500 . The operations of the illustrated process provided below are intended to be exemplary. In some embodiments, process 500 may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order of the operations of the process as illustrated in FIG. 5 and described below is not intended to be limiting.

At 501 , a topology map may be obtained from a scheduling system. Operation 501 may be similar to operation 401, and a detailed description thereof will be omitted herein.

At 502 , the topology map is preprocessed, and the modified topology map is modified with serial numbers of nodes occupied by the plurality of AGVs, motion state information of the plurality of AGVs, and node information of nodes of the topology map (eg, turn ability) can be obtained. Operation 502 may be similar to operation 501 , and a detailed description thereof will be omitted herein.

At 503 , the departure node and destination node of the current AGV in the scheduling system may be determined from the (modified) topology map based on the serial number of the node currently occupied by the AGV and the destination of the task currently assigned to the AGV.

At 504 , it may be determined whether the originating node or the destination node is a barrier. In response to determining that the originating node or destination node is a barrier, process 500 may proceed to operation 505 . In response to determining that the originating node and destination node are not barriers, process 500 may proceed to operation 506 .

At 505 , it may be determined that the route planning has failed.

가 결정될 수 있고 (수정된) 토폴로지 맵에서 경로 계획을 위해 검색될 노드들을 저장하는 데 사용될 수 있으며; 폐쇄 세트

가 경로 계획을 위해 이미 검색된 노드들을 저장하기 위해 결정되고 사용될 수 있다. At 506, open set

can be determined and used to store nodes to be searched for path planning in the (modified) topology map; closed set

may be determined and used to store nodes already found for path planning.

At 507 , the starting node may be designated as the current node, a total cost corresponding to the current node may be determined, and the starting node may be included in the open set.

At 508 , a binary heap structure may be determined, a node with a minimum total cost may be determined from among the nodes in the open set based on the binary heap structure, and the node may be deleted from the open set and added to the closed set.

At 509 , it may be determined whether the node with the minimum total cost is the destination node. In response to determining that the node with the minimum total cost is the destination node, process 500 may proceed to operation 510 . In response to determining that the node with the minimum total cost is not the destination node, process 500 may proceed to operation 511 .

At 510 , it may be determined that the route planning was successful. Previous nodes of the destination node may be determined until the previous node becomes the departure node.

At 511 , a next node of the current node may be determined, a total cost of the next node may be determined, and the next node may be included in the open set. The process may then proceed to operation 508 .

The next node of the current node moves in four directions (eg, upward, downward, left, right) of the current node based on the node information (eg, passability) of the nodes in the (modified) topology map. ) can be determined by determining the total costs associated with the neighboring nodes.

6 is a block diagram illustrating an exemplary processing device in accordance with some embodiments of the present disclosure; The processing device 112 may include a location obtaining module 610 , a map obtaining module 620 , a motion state obtaining module 630 , and a path determining module 640 .

The location obtaining module 610 may be configured to obtain a starting location and a destination of the target vehicle (eg, the vehicle 130 ). In some embodiments, the starting location and/or destination may be associated with a task assigned to the target vehicle. In some embodiments, the starting location may be the current location of the target vehicle, and the destination may be a location where the object is located. In some embodiments, the departure location may be a location where the object is located, and the destination may be the delivery location of the object. In some embodiments, the departure location may be the current location of the target vehicle and the destination may be the delivery location of the target.

The map acquisition module 620 may be configured to acquire a map of the target area including the starting location and the destination. In some embodiments, the map may include a topological map, a grid map, a geometric map, a point cloud map, and the like. In some embodiments, the map may include a plurality of nodes and node information (travelability) of each of the plurality of nodes in the map. In some embodiments, the map acquisition module 620 may acquire a map from a storage device (eg, the storage device 150 ) of the route planning system 100 , an external device, or the like. In some embodiments, the map acquisition module 620 may acquire a map by extracting a part of a preliminary map (which may correspond to an area larger than the target area) corresponding to the target area. Additionally or alternatively, the map acquisition module 620 may acquire the entire preliminary map.

The motion status acquisition module 630 may be configured to acquire motion status information associated with one or more vehicles different from the target vehicle in the target area. In some embodiments, the motion state associated with each of the one or more vehicles may include a stationary state and a moving state. In some embodiments, the motion status obtaining module 630 may obtain motion status information based on location information of one or more vehicles. Taking a specific vehicle as an example, when the position of the specific vehicle remains the same within a certain period of time (eg, 5 seconds, 20 seconds, 30 seconds, 1 minute, 2 minutes, 5 minutes), the motion state acquisition module 630 may determine that the particular vehicle is in a stationary state; On the other hand, when the location of the specific vehicle changes with time, the motion state obtaining module 630 may determine that the specific vehicle is in a moving state. In some embodiments, one or more vehicles may transmit motion status information to motion status obtaining module 630 via network 120 .

The route determination module 640 may be configured to determine a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with the one or more vehicles, and the map. In some embodiments, the path determination module 640 may determine the target path using a path planning algorithm. The path planning algorithm may include an A-star algorithm, a modified A-star algorithm, a Dijkstra algorithm, a Floyd-Warshall algorithm, a Bellman-Ford algorithm, a Rapid-Exploring Random Tree (RRT) algorithm, etc., or any combination thereof. have.

In some embodiments, for at least one of the plurality of nodes, the path determination module 640 determines at least a portion of each node information of at least one of the plurality of nodes based on the motion state information associated with the one or more vehicles. By modifying the target node information can be determined. Next, the path determination module 640 may generate a target map based on the map and target node information of at least one of the plurality of nodes. Also, the path determination module 640 may determine the target path of the target vehicle based on the target map, the departure location, and the destination.

In some embodiments, the route determination module 640 may determine a departure node from the plurality of nodes based on a departure location in the target map and determine a destination node from the plurality of nodes based on the destination in the target map. can As used herein, a departure node is the closest available node (eg, a location of a target vehicle) to a departure location (eg, a target vehicle) that is not occupied by one or more vehicles other than the target vehicle and is not occupied by a barrier. node), and the destination node may be an available node closest to the destination. Also, the route determination module 640 may determine the target route of the target vehicle based on a plurality of costs associated with a plurality of target nodes between the departure node and the destination node. As used herein, the cost of a node may refer to the cost of a path from an origin node to a destination node if the path includes the node.

The modules of the processing device 112 may be connected or communicate with each other via a wired connection or a wireless connection. Wired connections may include metal cables, optical cables, hybrid cables, etc., or any combination thereof. The wireless connection may include a local area network (LAN), a wide area network (WAN), Bluetooth, Zigbee, near field communication (NFC), etc., or any combination thereof. Two or more modules may be combined into a single module, and any of the modules may be divided into two or more units. For example, the location acquisition module 610 and the map acquisition module 620 may be combined into a single module, which acquires both the departure location and destination of the target vehicle, and a map of the target area including the departure location and destination. can As another example, the processing device 112 can include a task assignment module (not shown) that can assign a task (eg, delivering goods) to a target vehicle. As a further example, processing device 112 may include a storage module (not shown) that may be used to store data generated by the modules described above.

7 is a flowchart illustrating an exemplary process for determining a target path of a target vehicle in accordance with some embodiments of the present disclosure. In some embodiments, process 700 may be implemented as a set of instructions (eg, an application) stored in storage ROM 230 or RAM 240 . The processor 220 and/or modules of FIG. 6 may execute a set of instructions, and when executing the instructions, the processor 220 and/or modules may be configured to perform the process 700 . The operations of the illustrated process provided below are intended to be exemplary. In some embodiments, process 700 may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed herein. Additionally, the order of the operations of the process as illustrated in FIG. 7 and described below is not intended to be limiting.

At 710 , processing device 112 (eg, location acquisition module 610 ) (eg, interface circuits or processing circuits of processor 220 ) connects to target vehicle (eg, vehicle 130 ) ) to obtain the starting location and destination.

In some embodiments, the departure location and/or destination is a task assigned to the target vehicle by the processing device 112 , eg, a task for delivering an object (eg, goods, food, passenger) to the delivery location. can be related to In some embodiments, the starting location may be a current location of the target vehicle and the destination may be a location where the target is located. In some embodiments, the departure location may be a location where the object is located, and the destination may be the delivery location of the object. In some embodiments, the departure location may be the current location of the target vehicle and the destination may be the delivery location of the target. Further discussion of assigning tasks can be found elsewhere in this disclosure (eg, FIG. 9 and its description).

At 720 , processing device 112 (eg, map acquisition module 620 ) (eg, interface circuits or processing circuits of processor 220 ) provides a map of the target area including the departure location and destination can be obtained. In some embodiments, the target area may include a plurality of environmental objects, eg, goods, roads, trees, vehicles, buildings, rivers, pedestrians, etc., or any combination thereof. Accordingly, the map may include map data representing environmental objects of the target area.

In some embodiments, the map data may include location information of a plurality of environment objects, semantic information of a plurality of environment objects, etc., or any combination thereof. Typical location information of the environmental object may include latitude information, longitude information, height information (or altitude information), and the like. Typical semantic information of the environmental object includes the serial number of the environmental object, the name of the environmental object, the type of the environmental object (eg, the type of road (eg, highway)), and attribute information of the environmental object (eg, speed limits on the road, whether "one-way" or not, turning limits), etc., or any combination thereof.

In some embodiments, the map may include a topological map, a grid map, a geometric map, a point cloud map, and the like. In some embodiments, a map may include a plurality of nodes, each of which is a specific point (eg, a corner, gateway, intersection, stop) or a specific unit of a target area (eg, a circle, rectangle, triangle) can be represented.

In some embodiments, the map data may also include node information of each of the plurality of nodes in the map. For example, the node information of a node includes the serial number of the node, the barcode of the node (eg, one-dimensional barcode, two-dimensional barcode (eg, QR (Quick Response) code)), and the location (latitude) of the node. , which can be expressed in terms of longitude, height (or elevation), the coordinates of that node, the weight value of that node, the traversability of that node (e.g., whether a vehicle is allowed to pass through that node), that node the turn ability of (eg, whether the vehicle is allowed to rotate at that node), the number of neighboring nodes (eg, 4, 8), the node's connectivity to neighboring nodes, etc., or any combination thereof may include.

In some embodiments, the serial number or barcode of a node may be unique and may be used to indicate the location of the node. In some embodiments, a higher weight value of a node may result in a higher total cost of the node, and thus a higher probability that a vehicle will bypass the node.

In some embodiments, the node information may be a default setting of the route planning system 100 or may be adjustable according to different circumstances. For example, for all or some of the plurality of nodes, a weight value may be set to a default value (eg, 0), and the passability of the node may be set to allow vehicles to pass through, The turn capability of may be set to allow vehicles to rotate in the node. As another example, for at least some of the plurality of nodes, the node information may relate to environmental conditions, such as passability restrictions (eg, road controls, one-way restrictions), turn restrictions, and the like. More descriptions of node information can be found elsewhere in this disclosure (eg, FIG. 10 and its description).

In some embodiments, processing device 112 may obtain a map from a storage device (eg, storage device 150 ) of route planning system 100 , an external device, or the like. In some embodiments, the processing device 112 may obtain the map by extracting a portion of the preliminary map corresponding to the target area (which may correspond to an area larger than the target area). Additionally or alternatively, the processing device 112 may obtain the entire preliminary map.

At 730 , the processing device 112 (eg, the motion state module 630 ) (eg, interface circuits or processing circuits of the processor 220 ) configures one or more vehicles other than the target vehicle in the target area. It is possible to obtain motion state information associated with the

In some embodiments, the motion state associated with each of the one or more vehicles may include a stationary state and a moving state. In some embodiments, processing device 112 may obtain motion state information based on location information of one or more vehicles. Taking a specific vehicle as an example, if the location of the specific vehicle remains the same within a period of time (eg, 5 seconds, 20 seconds, 30 seconds, 1 minute, 2 minutes, 5 minutes), the processing device 112 ) may determine that a particular vehicle is in a stationary state; On the other hand, if the location of the particular vehicle changes over time, the processing device 112 may determine that the particular vehicle is in a moving state. In some embodiments, one or more vehicles may transmit motion state information to processing device 112 via network 120 .

At 740 , the processing device 112 (eg, the route determination module 640 ) (eg, processing circuitry of the processor 220 ) configures the starting location, destination, motion state information associated with one or more vehicles, and A target path of the target vehicle may be determined based at least in part on the map.

In some embodiments, processing device 112 may determine a target path using a path planning algorithm. The path planning algorithm may include an A-star algorithm, a modified A-star algorithm, a Dijkstra algorithm, a Floyd-Warshall algorithm, a Bellman-Ford algorithm, a Rapid-Exploring Random Tree (RRT) algorithm, etc., or any combination thereof. have.

In some embodiments, for at least one of the plurality of nodes, the processing device 112 modifies at least a portion of respective node information of at least one of the plurality of nodes based on motion state information associated with the one or more vehicles. By doing so, target node information can be determined. Further descriptions relating to modifying node information can be found elsewhere in this disclosure (eg, FIG. 8 and its description). The processing device 112 may then generate the target map based on the map and target node information of at least one of the plurality of nodes. Further, the processing device 112 may determine a target route of the target vehicle based on the target map, the departure location, and the destination.

In some embodiments, processing device 112 may determine a departure node from a plurality of nodes based on a departure location in the target map and determine a destination node from the plurality of nodes based on a destination in the target map. have. As used herein, a departure node is the closest available node (eg, a location of a target vehicle) to a departure location (eg, a target vehicle) that is not occupied by one or more vehicles other than the target vehicle and is not occupied by a barrier. node), and the destination node may be an available node closest to the destination. Further, the processing device 112 may determine the target path of the target vehicle based on a plurality of costs associated with the plurality of target nodes between the departure node and the destination node. As used herein, the cost of a node may refer to the cost of a path from an origin node to a destination node if the path includes the node.

By way of example only, and as described with respect to FIG. 4 , the processing device 112 may determine a first set (eg, an open set) and a second set (eg, a closed set), the first A cycle may be performed based on the set and the second set. In the initialization state, the first set contains only the starting nodes, and the second set is an empty set. In the first cycle, the processing device 112 may move the starting node to the second set and add at least one associated node of the starting node to the first set. As used herein, for a particular node, an associated node may refer to a neighboring node reachable (eg, traversable, turnable) from the particular node. For example, as shown in FIG. 10 , for node 1, at least one related node includes node 2, node 3, node 4, and node 5. Further, the processing device 112 may determine at least one cost of the at least one associated node and move the associated node with the least cost to the second set. The processing device 112 may then determine whether the associated node with the least cost is the destination node. In response to determining that the associated node is the destination node, the processing device 112 may determine the parent node of the associated node (which is the starting node of the first cycle) and sequentially cycle through the parent node of the parent node until the departure node is reached. can be determined as Further, the processing device 112 may determine, as the target route, a route from the origin node to the destination including intermediate nodes (ie, the associated nodes with the least cost in multiple cycles). In response to determining that the associated node is not the destination node, the processing device 112 may designate the associated node as the current node and execute the next cycle.

In the ith cycle, for an associated node with the minimum cost determined in the previous cycle (which may also be considered an ith “current node”), the processing device 112 determines at least one associated node of the current node and determines at least one A related node of may be added to the first set. The processing device 112 may then determine at least one cost of the at least one associated node. Further, for each of the at least one associated node, the processing device 112 may determine whether the associated node is already in the first set. In response to determining that the related node is already in the first set, the processing device 112 determines that the cost of the related node determined in the current cycle (also referred to as the “current cost”) is the cost of the related node determined in the previous cycle (also referred to as the “previous cost”). cost") can be determined. In response to determining that the associated node's current cost is less than the associated node's previous cost, the processing device 112 updates the associated node's cost and sets the associated node's parent node (which may be the previous current node in the previous cycle). You can update to the current node. In response to determining that the associated node's current cost is greater than or equal to the associated node's previous cost, processing device 112 may maintain the associated node's parent node and cost for further processing. In response to determining that the associated node is not in the first set, processing device 112 may determine the parent node of the associated node to be the current node. Further, the processing device 112 may determine the associated node with the least cost and determine whether the associated node with the least cost is the destination node. Similarly above, in response to determining that the associated node is a destination node, processing device 112 may determine the parent node of the associated node, and sequentially determine the parent node of the parent node until a departure node is reached. . Further, the processing device 112 may determine, as the target route, a route from the origin node to the destination including intermediate nodes (ie, the associated nodes with the least cost in multiple cycles). In response to determining that the associated node is not the destination node, the processing device 112 may designate the associated node as the next current node, and may execute a next cycle until the updated associated node becomes the destination node.

In some embodiments, the processing device 112 determines the cost of the node based on a first cost from the starting node to the corresponding node and a second cost from the node to the destination node in determining the cost of the node. can For example, the processing device 112 may determine the cost of the node according to equation (6) below:

(6)

(6)

은 해당 노드의 일련 번호를 나타내고,

은 해당 노드의 비용를 나타내고,

은 출발 노드로부터 해당 노드까지의 제1 비용을 나타내고,

은 해당 노드로부터 목적지 노드까지의 제2 비용을 나타낸다.here

represents the serial number of the node,

represents the cost of the node,

represents the first cost from the starting node to the corresponding node,

denotes the second cost from the corresponding node to the destination node.

In some embodiments, the processing device 112 may determine the first cost according to Equation (7) below:

(7)

(7)

은 노드의 가중치 값을 나타내고,

는 해당 노드의 부모 노드의 비용을 나타내고,

은 해당 노드의 부모 노드로부터 해당 노드까지의 비용을 나타내고,

은 해당 노드의 부모 노드로부터 해당 노드로의 턴 비용을 나타낸다.here

represents the weight value of the node,

represents the cost of that node's parent node,

represents the cost from the parent node of the node to the node,

represents the turn cost from the parent node of the node to the node.

In some embodiments, the processing device 112 may determine the cost from the parent node of the node to the node according to Equation (8) below:

(8)

(8)

및

는 해당 노드의 부모 노드의 X 좌표 및 Y 좌표를 각각 나타내고,

및

은 해당 노드의 X 좌표 및 Y 좌표를 각각 나타낸다.here

and

represents the X and Y coordinates of the parent node of that node, respectively,

and

represents the X coordinate and Y coordinate of the corresponding node, respectively.

In some embodiments, the processing device 112 may determine the turn cost from the parent node of the node to the node according to Equation (9) below:

(9)

(9)

는 해당 노드의 부모 노드의 타겟 각도를 나타내고,

은 해당 노드의 타겟 각도를 나타내고,

는 턴 비용의 가중 인자를 나타낸다.here

represents the target angle of the parent node of the corresponding node,

represents the target angle of the node,

denotes the weighting factor of the turn cost.

In some embodiments, the processing device 112 may determine the second cost from that node to the destination node according to Equation (10) below:

(10)

(10)

및

는 목적지 노드의 X 좌표 및 Y 좌표를 각각 나타낸다. 해당 노드로부터 목적지 노드까지의 제2 비용은 맨해튼 거리에 기초하여 결정될 수 있음을 알 수 있다. 맨해튼 거리는 설명의 목적으로 제공되었으며, 제2 비용은 또한 다른 거리들, 예를 들어, 유클리드 거리 들에 기초하여 결정될 수 있다.here

and

denotes the X coordinate and Y coordinate of the destination node, respectively. It can be seen that the second cost from the corresponding node to the destination node may be determined based on the Manhattan distance. The Manhattan distance is provided for illustrative purposes, and the second cost may also be determined based on other distances, eg, Euclidean distances.

It should be noted that the above description is provided for purposes of illustration only, and is not intended to limit the scope of the present disclosure. Numerous variations and modifications may be made to those skilled in the art under the teachings of this disclosure. However, these changes and modifications do not depart from the scope of the present disclosure. For example, the processing device 112 may perform operation 720 before operation 710 , or may perform operations 710 and 720 concurrently. As another example, the processing device 112 may automatically update the target node information based on the newly acquired motion state information according to a predetermined time interval (eg, 1 minute, 2 minutes, 5 minutes), or Alternatively, the target node information may be updated when a request for updating the target node information is received.

8 is a flowchart illustrating an exemplary process for determining a target path of a target vehicle in accordance with some embodiments of the present disclosure. In some embodiments, process 800 may be implemented as a set of instructions (eg, an application) stored in storage ROM 230 or RAM 240 . The processor 220 and/or modules of FIG. 6 may execute a set of instructions, and when executing the instructions, the processor 220 and/or modules may be configured to perform the process 800 . The operations of the illustrated process provided below are intended to be exemplary. In some embodiments, process 800 may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed herein. Additionally, the order of the operations of the process as illustrated in FIG. 8 and described below is not intended to be limiting.

At 801 , the processing device 112 (eg, the path determination module 640 ) (eg, processing circuits of the processor 220 ) receives the one or more stationary vehicles from the one or more vehicles based on the motion state information. Vehicles can be identified. As described with respect to operation 730, the motion state associated with each of the one or more vehicles may include a stationary state and a moving state. Accordingly, one or more stationary vehicles are vehicle(s) in a stationary state.

At 802 , for each of the one or more stationary vehicles, processing device 112 (eg, route determination module 640 ) (eg, processing circuits of processor 220 ) associated with the stationary vehicle A preset area including one or more nodes (eg, an area centered on a stationary vehicle) may be determined. In some embodiments, processing device 112 may designate an area occupied by a stationary vehicle as a preset area. In some embodiments, the processing device 112 may determine the preset area by expanding (eg, proportionally expanding) the area occupied by the stationary vehicle. In some embodiments, the preset area size and/or shape may be a default setting of the route planning system 100 or may be adjustable according to other circumstances. For example, the larger the size of the stopped vehicle, the larger the preset area may be. As another example, the shorter the distance between two adjacent nodes in the map, the larger the preset area may be. As another example, the shape of the preset region may include a circle, a rectangle, a triangle, or any regular or irregular shape.

At 803 , for each of the one or more nodes associated with the stationary vehicle, the processing device 112 (eg, the path determination module 640 ) (eg, processing circuits of the processor 220 ) of the node You can edit node information. In some embodiments, the processing device 112 may assign to the node a weight value that is greater than a predetermined threshold. In some embodiments, a higher weight value of a node may result in a higher total cost of the node, and thus a higher probability that a vehicle will bypass the node. In some embodiments, processing device 112 may also designate a node as a non-turnable node (ie, a vehicle is not allowed to turn left or right at that node).

At 804 , processing device 112 (eg, route determination module 640 ) (eg, processing circuits of processor 220 ) configures the map and one or more nodes associated with each of the one or more stationary vehicles. A candidate map may be determined based on the modified node information. For example, the processing device 112 may generate the candidate map by updating node information of one or more nodes associated with each of the one or more stationary vehicles with the modified node information.

At 805 , the processing device 112 (eg, the route determination module 640 ) (eg, processing circuits of the processor 220 ) configures the candidate of the target vehicle based on the candidate map, the departure location, and the destination. path can be determined. As described with respect to operation 740 , the processing device 112 may determine a candidate departure node from the plurality of nodes based on a departure location of the candidate map, and may determine a candidate from the plurality of nodes based on a destination of the candidate map. The destination node may be determined, and the candidate path may be determined based on the candidate departure node and the candidate destination node.

In some embodiments, the processing device 112 may determine whether the node closest to the starting location or the node closest to the destination is a barrier or is being occupied by vehicles other than the target vehicle. In response to determining that both the node closest to the departure location and the node closest to the destination are not barriers and are not occupied by other vehicles, the processing device 112 selects the node closest to the departure location and the node closest to the destination. It can be designated as a departure node and a candidate destination node, respectively. Responsive to determining that the node closest to the starting location is a barrier or occupied by a vehicle other than the target vehicle, the processing device 112 may determine the next closest node to the starting location, and an available node identifies may determine whether the next closest node is a barrier or is not occupied by a vehicle other than the target vehicle, or the processing device 112 may wait until the nearest node is released. In response to determining that the node closest to the destination is a barrier or is occupied by a vehicle other than the target vehicle, the processing device 112 may determine that the route planning has failed, or the processing device 112 may determine that the closest node You can wait until it is released. Additionally or alternatively, the processing device 112 may determine an available node within a predetermined distance (eg, 20m, 50m, 70m, 100m) from the destination as a candidate destination node.

In some embodiments, as described with respect to operation 740 , the processing device 112 may determine a candidate path based on a plurality of costs associated with a plurality of target nodes between the candidate originating node and the candidate destination node. have.

At 806 , the processing device 112 (eg, the path determination module 640 ) (eg, processing circuits of the processor 220 ) configures at least a portion of at least one predetermined path through which the candidate path has not been traversed. and at least partially overlap with . As used herein, the at least one predetermined route may correspond to a moving vehicle of at least one of the one or more vehicles.

In response to determining that the candidate path does not overlap a portion of the at least one predetermined path that has not been traversed, at 807 , the processing device 112 (eg, the path determination module 640 ) (eg, the processor ( The processing circuits of 220 ) may designate the candidate route as the target route of the target vehicle.

In response to determining that the candidate path at least partially overlaps with at least a portion of the at least one predetermined path not traversed, at 808 , for each of the one or more nodes associated with the stopped vehicle, the processing device 112 (eg, , the path determination module 640 ) (eg, processing circuits of the processor 220 ) may further modify node information of the node. In some embodiments, processing device 112 may designate a node as a barrier.

At 809 , the processing device 112 (eg, the route determination module 640 ) (eg, processing circuits of the processor 220 ) is configured to move one or more vehicles from the one or more vehicles based on the motion state information. Vehicles can be identified. The one or more moving vehicles are vehicle(s) in a moving state.

At 810 , for each of the one or more moving vehicles, the processing device 112 (eg, the route determination module 640 ) (eg, processing circuits of the processor 220 ) corresponds to the moving vehicle. may determine one or more nodes associated with the predetermined path. For example, the processing device 112 may determine all nodes on the portion of the predetermined path not traversed by the moving vehicle. As another example, the processing device 112 may determine some of the nodes (eg, top N) on a portion of a predetermined path not traversed by a moving vehicle. Additionally or alternatively, for each of the one or more moving vehicles, the processing device 112 may determine a preset area associated with the moving vehicle (eg, an area centered on the moving vehicle that is associated with a stationary vehicle). may be determined in a manner similar to the method for determining the preset region), and one or more nodes within the preset region may be determined.

At 811 , for each of the one or more nodes associated with the moving vehicle, the processing device 112 (eg, the route determination module 640 ) (eg, processing circuits of the processor 220 ) of the node You can edit node information. In some embodiments, the processing device 112 assigns to the node a weight value that is greater than a predetermined threshold (which may be different from or equal to a predetermined threshold corresponding to a weight value associated with a stationary vehicle described above). can do.

At 812 , processing device 112 (eg, route determination module 640 ) (eg, processing circuits of processor 220 ) displays a map of one or more nodes associated with each of the one or more stationary vehicles. The second candidate map may be determined based on the modified node information and the modified node information of one or more nodes associated with each of the one or more moving vehicles. For example, the processing device 112 may generate the second candidate map by updating node information of one or more nodes associated with each of the one or more stationary vehicles and the one or more moving vehicles with the modified node information.

At 813 , the processing device 112 (eg, the route determination module 640 ) (eg, processing circuits of the processor 220 ) configures the target vehicle based on the second candidate map, the departure location, and the destination. may determine a second candidate path of . As described with respect to operations 740 and 805 , the processing device 112 may determine a second candidate departure node from the plurality of nodes based on a departure location of the second candidate map, and a destination of the second candidate map. The second candidate destination node may be determined from the plurality of nodes based on , and the second candidate path may be determined based on the second candidate departure node and the second candidate destination node. In some embodiments, the second candidate starting node may be the same as or different from the aforementioned candidate starting node; Similarly, the second candidate destination node may be the same as or different from the aforementioned candidate destination node. For example, if the candidate departure node is not a barrier in the second candidate map or is not occupied by other vehicles, the second candidate departure node may be the same as the candidate departure node.

In some embodiments, the processing device 112 may directly designate the second candidate path as the target path. In some embodiments, the processing device 112 may also determine whether the second candidate path at least partially overlaps with at least a portion of the at least one predetermined path that was not traversed. In response to determining that the second candidate path does not overlap a portion of the at least one predetermined path that was not traversed, the processing device 112 may designate the second candidate path as the target path. In response to determining that the second candidate path at least partially overlaps with at least a portion of the at least one predetermined path that was not traversed, the processing device 112 is further configured to modify at least a portion of the node information in the second candidate map to obtain a second The candidate map may be updated, and route planning may be performed based on the updated map.

It should be noted that the above description is provided for purposes of illustration only, and is not intended to limit the scope of the present disclosure. Numerous variations and modifications may be made to those skilled in the art under the teachings of this disclosure. However, these changes and modifications do not depart from the scope of the present disclosure. For example, operations 801-807 may be omitted, and the processing device 112 may provide node information of node(s) associated with one or more stationary vehicles and node(s) associated with one or more moving vehicles. By modifying the node information, a second candidate map (which may be regarded as a “target map”) may be directly generated, and a target path may be determined based on the second candidate map.

9 is a flowchart illustrating an exemplary process for allocating a task in accordance with some embodiments of the present disclosure. In some embodiments, process 900 may be implemented as a set of instructions (eg, an application) stored in storage ROM 230 or RAM 240 . The processor 220 and/or modules of FIG. 6 may execute a set of instructions, and when executing the instructions, the processor 220 and/or modules may be configured to perform the process 900 . The operations of the illustrated process provided below are intended to be exemplary. In some embodiments, process 900 may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed herein. Additionally, the order of the operations of the process as illustrated in FIG. 9 and described below is not intended to be limiting.

At 910 , processing device 112 (eg, task assignment module) (eg, interface circuits or processing circuits of processor 220 ) may acquire the task. In some embodiments, the task may be a task to deliver an object (eg, merchandise, food, passenger) to a shipping location.

At 920 , processing device 112 (eg, task assignment module) (eg, processing circuits of processor 220 ) may determine whether the task is a designated task.

In response to determining that the task is an unassigned task, at 930 , the processing device 112 (eg, the task assignment module) (eg, processing circuits of the processor 220 ) configures a schedulable for the task. You can select a vehicle (ie, no tasks are being executed or running by that vehicle). For example, the processing device 112 may select a schedulable vehicle closest to the delivery location.

At 940 , processing device 112 (eg, task assignment module) (eg, processing circuits of processor 220 ) may assign a task to the schedulable vehicle.

In response to determining that the task is a designated task, at 950 , processing device 112 (eg, task assignment module) (eg, processing circuits of processor 220 ) determines that the designated vehicle corresponding to the designated task You can decide whether it is schedulable or not.

In response to determining that the designated vehicle corresponding to the designated task is schedulable, at 960 , the processing device 112 (eg, task assignment module) (eg, processing circuits of the processor 220 ) configures the designated task can be assigned to the corresponding designated vehicle.

In response to determining that the designated vehicle corresponding to the designated task is in a non-schedulable state, at 970 , processing circuits of the processing device 112 (eg, task assignment module) (eg, processor 220 ) ) assigns the specified task to the corresponding specified vehicle until the vehicle becomes schedulable.

In some embodiments, prior to assigning the designated task to the corresponding designated vehicle, the processing device 112 may determine whether the location reported by the designated vehicle is correct. In response to determining that the location reported by the corresponding designated vehicle is correct, the processing device 112 may assign the designated task to the corresponding designated vehicle. In response to determining that the location reported by the corresponding designated vehicle is incorrect, the processing device 112 may determine that the task assignment has failed. Additionally or alternatively, the processing device 112 may use the positioning device to obtain the exact location of the designated vehicle and assign the designated task to the designated vehicle.

It should be noted that the above description is provided for purposes of illustration only, and is not intended to limit the scope of the present disclosure. Numerous variations and modifications may be made to those skilled in the art under the teachings of this disclosure. However, these changes and modifications do not depart from the scope of the present disclosure.

10 is a schematic diagram illustrating typical node information of a node according to some embodiments of the present disclosure. 10 , taking node 1 as an example, node 1 has a direction from node 4 to node 2, from node 2 to node 5, from node 5 to node 3, and from node 3 to node 5. can pass through Also, it can be seen that nodes adjacent to node 1 include node 2, node 3, node 4, and node 5. As indicated by the straight arrows, node 1 is interconnected with 4 adjacent nodes, ie, 4 adjacent nodes are reachable from node 1. Also, node 1 can also rotate as indicated by the circular arrow.

11 is a schematic diagram illustrating exemplary nodes associated with a stationary vehicle and exemplary nodes associated with a moving stationary vehicle in accordance with some embodiments of the present disclosure.

11 , an area 1110 refers to a target area including a departure location and a destination, 1120 refers to a vehicle stopped in the target area (indicated by vehicle B), and 1140 refers to a moving vehicle in the target area. Refers to a vehicle (denoted as vehicle C). 8 , for a stationary vehicle 1120 , the processing device 112 may determine a preset area 1130 centered on the stationary vehicle 1120 . It can be seen that the preset region 1130 includes a node 14 , a node 15 , and a node 16 . Also, for each of the three nodes, the processing device 112 may modify the node's node information as described elsewhere in this disclosure. For a moving vehicle 1140 , the processing device 112 may obtain a portion of a predetermined route (indicated by an arrow) corresponding to the moving vehicle to be passed in a predetermined future time period. It can be seen that some of the predetermined paths include node 12, node 18, node 24, and node 30 in the dotted line box. Also, for each of the four nodes, the processing device 112 may modify the node's node information as described elsewhere in this disclosure.

In some embodiments, the present disclosure may also provide a storage medium storing a computer program. When executed by a processor, the computer program may instruct the processor to perform a process (eg, process 400 , process 500 , process 700 , process 800 , process 900 ) described elsewhere in this disclosure.

In some embodiments, the present disclosure may also provide an electronic device comprising a processor and a storage, the storage storing a computer program. When executed by a processor, the computer program may instruct the processor to perform a process (eg, process 400 , process 500 , process 700 , process 800 , process 900 ) described elsewhere in this disclosure.

Having described basic concepts accordingly, after reading this detailed disclosure, it may be fairly apparent to those skilled in the art that the foregoing detailed disclosure is intended to be provided by way of example only and not limitation. Various changes, improvements, and modifications may occur and are intended to those skilled in the art, although not explicitly described herein. These changes, improvements, and modifications are intended to be suggested by the present disclosure and are within the spirit and scope of exemplary embodiments of the present disclosure.

Moreover, specific terminology has been used to describe embodiments of the present disclosure. For example, the terms (“one embodiment,” “an embodiment,” and/or “some embodiments”) indicate that a particular feature, structure, or characteristic described in connection with an embodiment relates to at least one embodiment of the present disclosure. means within. Therefore, it should be emphasized and understood that references to "an embodiment" or "one embodiment" or "alternative embodiment" in various places in the various parts of this specification are not necessarily all referring to the same embodiment. . Moreover, certain features, structures, or characteristics may be suitably combined in one or more embodiments of the present disclosure.

In addition, aspects of the present disclosure may be illustrated and described in any of a number of patentable classes or contexts, including any new and useful process, line, line, manufacture, or composition of matter, or any new and useful improvement thereof. It will be appreciated by those skilled in the art. Accordingly, aspects of the present disclosure may be entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or all software and It may be implemented by combining hardware implementations. Moreover, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signal having computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium and is any computer-readable medium that can convey, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. can be The program code embodied on a computer readable signal medium may be transmitted using any suitable medium, including wireless, wireline, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The computer program code for executing the operations for aspects of the present disclosure may be an object-oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., a "C" programming language, a visual Any of one or more programming languages, including BASIC, Fortran 2003, Perl, COBOL 2002, existing procedural programming languages such as PHP, ABAP, dynamic programming languages such as Phythod, Ruby and Groovy, or other programming languages combinations can be recorded. The program code may run entirely on the user's computer, partly on the user's computer, as a standalone software package, partly on the user's computer and partly on the remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (eg, over the Internet using an Internet service provider) or in a cloud computing environment, or provided as a service, such as software-as-a-service (SaaS).

Moreover, the listed order of processing elements or sequences, or the use of numbers, letters, or other designations, is therefore intended to limit the claimed processes and methods to any order except as may be specified in the claims. not intended While the above disclosure discusses, by way of various examples, what are presently considered to be various useful embodiments of the present disclosure, these details are for that purpose only and the appended claims are not limited to the disclosed embodiments, in contrast thereto. It will be understood that it is intended to cover modifications and equivalent arrangements falling within the spirit and scope of the disclosed embodiments. For example, implementation of the various components described above may be embodied in a hardware device, but it may also be implemented as a software-only solution, such as installation on an existing server or mobile device.

Similarly, in the foregoing description of embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure when assisting in understanding one or more of the various embodiments. It should be understood that This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, claimed subject matter may lie in less than all features of a single previously disclosed embodiment.

Claims (48)

A method for route planning for an Automatic Guided Vehicle (AGV) based on a topology map, comprising:
obtaining a topology map corresponding to the scheduling system;
Modification by preprocessing the topology map to modify node information of the topology map based on motion state information associated with a plurality of AGVs in the scheduling system and a plurality of nodes occupied by the plurality of AGVs in the topology map obtaining a topology map;
determining a departure node and a destination node of the current AGV from the modified topology map;
determining a plurality of total costs associated with a plurality of nodes between the departure node and the destination node based on node information of the modified topology map; and
and performing route planning for the current AGV based on the plurality of total costs. The method of claim 1, wherein the motion state information includes a moving state and a stationary state, and preprocessing the topology map comprises:
performing a first preprocessing on the topology map, comprising:
for each of the stationary AGVs different from the current AGV in the topology map, the node information of one or more nodes within a preset area centered on the stationary AGV is modified; and
performing the first preprocessing by generating a first modified topology map; and/or
performing a second preprocessing on the topology map, comprising:
for each of the stationary AGVs different from the current AGV in the topology map, modifying node information of one or more nodes within a preset area centered on the stationary AGV;
modifying, for each of the moving AGVs in the topology map, node information of one or more nodes of a planned path not traversed by the moving AGV; and
and performing the second pre-processing by generating a second modified topology map. The method of claim 2, further comprising: determining a departure node and a destination node of the current AGV in the modified topology map; determining a plurality of associated total costs, and performing route planning for the current AGV based on the plurality of total costs:
determining a first departure node and a first destination node of a current AGV in the first modified topology map;
determining a plurality of first total costs associated with a plurality of first nodes between the first departure node and the first destination node based on node information of the first modified topology map;
performing a first route planning for the current AGV based on the plurality of first total costs;
In response to detecting that the current AGV collides with at least one of one or more moving AGVs when the current AGV moves along the first path of the first path plan, the current AGV in a second modified topology map determining a second departure node and a second destination node of
determining a plurality of second total costs associated with a plurality of second nodes between the second departure node and the second destination node based on node information of the second modified topology map; and
and performing a second route planning for the current AGV based on the plurality of second total costs. The method of claim 2, wherein, for each of the stationary AGVs different from the current AGV in the topology map, modifying node information of one or more nodes within a preset area centered on the stationary AGV;
For each of the stationary AGVs different from the current AGV in the topology map,
modifying one or more weight values of one or more nodes in a preset area centered on the stationary AGV to one or more high weight values, each of the one or more high weight values being greater than a preset threshold value, the modifying step , and
and configuring the one or more nodes as non-turnable nodes. 3. The method of claim 2,
for each of the stationary AGVs different from the current AGV in the topology map, modifying node information of one or more nodes within a preset area centered on the stationary AGV; and
For each of the moving AGVs in the topology map, modifying node information of one or more nodes of a planned path not traversed by the moving AGV includes:
For each of the stationary AGVs different from the current AGV in the topology map,
setting one or more nodes within a preset area centered on the stationary AGV as impassable nodes; and
designating the one or more nodes as one or more barriers; and
For each of the moving AGVs different from the current AGV in the topology map, setting one or more weight values of one or more nodes of a planned path not traversed by the moving AGV to one or more high weight values, wherein the one or more high weight values and each of the above high weight values is greater than a preset threshold value. The method according to claim 1, wherein after determining a departure node and a destination node of the current AGV in the modified topology map:
Further comprising the step of determining the failure of the route planning when the departure node or the destination node is determined to be a barrier based on the node information of the modified topology map. How to plan a route. The method of claim 1 , further comprising: determining a plurality of total costs associated with a plurality of nodes between the departure node and the destination node based on node information of the modified topology map; and based on the plurality of total costs. The steps of performing route planning for the current AGV by:
designating the departure node as a first current node of the route;
For each cycle, determining an updated current node and determining that the updated current node is not a destination node:
determining one or more total costs corresponding to one or more nodes other than nodes in the path;
determining the node with the minimum total cost as the node on the path and next to the updated current node; and
and determining a next node of the updated current node by determining the next node as the updated current node in a next cycle. 8. The method of claim 7, wherein determining one or more total costs corresponding to one or more nodes other than nodes in the path comprises:
determining, for each of the one or more nodes, a total cost corresponding to that node by determining the sum of the cost from the current node to the corresponding node and the cost from the node to the destination node. How to plan routes for self-guided vehicles. The method of claim 8, wherein the cost from the current node to the corresponding node is a total cost corresponding to the current node, a weight value of the corresponding node, a distance cost determined based on the distance from the current node to the corresponding node, and the current A route planning method for an autonomously guided vehicle based on a topology map, which is determined based on a turn cost from a node to that node. 8. The method of claim 7, wherein determining the node with the minimum total cost comprises:
A route planning method for an autonomously guided vehicle based on a topology map, comprising determining a node with a minimum total cost based on a binary heap structure. The method of claim 1 , wherein before determining a departure node and a destination node of the current AGV in the modified topology map, the method further comprises:
obtaining an assigned task by the scheduling system, the task including a designated task and an unassigned task;
Determining the destination node of the current AGV based on the modified topology map comprises:
and determining the destination node based on a designated task and an unassigned task of the current AGV. The method of claim 1 , wherein determining the starting node of the current AGV based on the modified topology map comprises:
determining a node closest to the current AGV; and
and designating the node as the departure node when not occupied by AGVs other than the current AGV. A device for route planning for an autonomously guided vehicle (AGV) based on a topology map, comprising:
at least one processor; and
at least one repository containing program codes;
13. A device, wherein the program codes instruct the at least one processor to execute the method of any one of claims 1 to 12 when the program codes are executed by the at least one processor. A computer readable storage medium comprising computer programs, the computer programs causing the method of any one of claims 1 to 12 to be executed. As a system for route planning:
at least one storage device comprising a set of instructions; and
at least one processor in communication with the at least one storage device;
The at least one processor, when executing the set of instructions, causes the system to:
obtain the starting position and destination of the target vehicle;
obtain a map of a target area including the departure location and the destination, the map including node information of each of a plurality of nodes;
obtain motion state information associated with one or more vehicles different from the target vehicle in the target area;
and determine a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with the one or more vehicles, and the map. The method according to claim 15, wherein the node information of each of the plurality of nodes includes the location of the node, the barcode of the node, the coordinates of the node, the passability of the node, turn capability of the node, and the number of adjacent nodes. number, or the node's connectivity to the adjacent nodes. 17. The method of claim 15 or 16, wherein the at least one of the at least one The processor of the system causes:
For at least one of the plurality of nodes, based on motion state information associated with the one or more vehicles, determine target node information by modifying at least a portion of each node information of at least one of the plurality of nodes, and ;
generate a target map based on the map and target node information of at least one of the plurality of nodes;
and determine a target route of the target vehicle based on the target map, the departure location, and the destination. 18. The system of claim 17, wherein to determine a target route of the target vehicle based on the target map, the departure location, and the destination, the at least one processor further causes the system to:
determine a departure node from the plurality of nodes based on a departure location in the target map;
determine a destination node from the plurality of nodes based on a destination in the target map;
determine a target route of the target vehicle based on a plurality of costs associated with a plurality of target nodes between the departure node and the destination node, wherein the cost of the node is a second cost from the departure node to the node. and a second cost from the node to the destination node. 19. The method of claim 18, wherein the first cost from the starting node to the node is the weight value of the node, the cost of the node's parent node, the cost of the node's parent node to the node, and the cost of the node in the target map. A system for path planning, determined based on a turn cost from a node's parent node to the node. 20. The method of any one of claims 15 to 19 for determining a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with one or more vehicles, and the map. , the at least one processor causes the system to:
identify one or more stationary vehicles from the one or more vehicles based on the motion state information;
determine, for each of the one or more stationary vehicles, a preset area comprising one or more nodes associated with the stationary vehicle;
for each of the one or more nodes associated with the stationary vehicle, modify node information of the node;
determine a candidate map based on the map and modified node information of one or more nodes associated with each of the one or more stationary vehicles;
and determine a candidate route of the target vehicle based on the candidate map, the departure location, and the destination. 21. The method of claim 20, wherein, for each of the one or more nodes associated with the stationary vehicle, to modify node information of the node, the at least one processor comprises:
assign a weight value greater than a predetermined threshold to the node;
A system for path planning, directed to designate the node as a non-turnable node. 22. The method of claim 20 or 21, wherein the at least one processor further causes the system to:
determine whether the candidate path at least partially overlaps with at least a portion of at least one predetermined path not traversed, wherein the at least one predetermined path corresponds to a moving vehicle of at least one of the one or more vehicles; ;
and in response to determining that the candidate path does not overlap with at least one untraveled predetermined path, direct the candidate path to a target path of the target vehicle. 23. The system of claim 22, wherein the at least one processor further causes the system to:
in response to determining that the candidate path at least partially overlaps with at least a portion of at least one predetermined path not traversed, modify node information of the node for each of the one or more nodes associated with the stationary vehicle;
identify one or more moving vehicles from the one or more vehicles based on the movement state information;
determine, for each of the one or more moving vehicles, one or more nodes associated with a predetermined path corresponding to the moving vehicle;
for each of the one or more nodes associated with the moving vehicle, modify node information of the node;
determine a second candidate map based on the map, modified node information of one or more nodes associated with each of the one or more stationary vehicles, and modified node information of one or more nodes associated with each of the one or more moving vehicles do;
and determine a second candidate route of the target vehicle based on the second candidate map, the departure location, and the destination. 24. The method of claim 23,
For each of the one or more nodes associated with the stationary vehicle, to modify node information of the node, the at least one processor causes the system to:
configured to designate the node as a barrier;
For each of the one or more nodes associated with the moving vehicle, to modify node information of the node, the at least one processor causes the system to:
and assign a weight value greater than a predetermined threshold to the node. 25. The method of any one of claims 15 to 24, further configured to determine a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with the one or more vehicles, and the map. In order to do so, the at least one processor also causes the system to:
identify one or more stationary vehicles from the one or more vehicles based on the motion state information;
determine, for each of the one or more stationary vehicles, a preset area comprising one or more nodes associated with the stationary vehicle;
for each of the one or more nodes associated with the stationary vehicle, modify node information of the node by designating the node as a barrier;
identify one or more moving vehicles from the one or more vehicles based on the movement state information;
determine, for each of the one or more moving vehicles, one or more nodes associated with a predetermined path corresponding to the moving vehicle;
modify node information of the node by assigning, for each of the one or more nodes associated with the moving vehicle, a weight value greater than a predetermined threshold to the node;
determine a target map based on the map, modified node information of one or more nodes associated with each of the one or more stationary vehicles, and modified node information of one or more nodes associated with each of the one or more moving vehicles;
and determine a target route of the target vehicle based on the target map, the departure location, and the destination. A method implemented in a computing device comprising at least one processor, at least one storage medium, and a communication platform coupled to a network, the method comprising:
obtaining a starting position and a destination of the target vehicle;
obtaining a map of a target area including the starting location and the destination, wherein the map includes node information of each of a plurality of nodes; obtaining a map of the target area;
acquiring motion state information associated with one or more vehicles different from the target vehicle in the target area; and
determining a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with the one or more vehicles, and the map. The method of claim 26, wherein the node information of each of the plurality of nodes includes the location of the node, the barcode of the node, the coordinates of the node, the traversability of the node, the turning ability of the node, the number of adjacent nodes, or the A method implemented on a computing device, comprising at least one of a node's connectivity to the adjacent nodes. 28. The method of claim 26 or 27, wherein determining a target path of the target vehicle based at least in part on the departure location, the destination, and motion state information associated with the one or more vehicles comprises:
determining, for at least one of the plurality of nodes, target node information by modifying at least a portion of each node information of at least one of the plurality of nodes based on motion state information associated with the one or more vehicles; ;
generating a target map based on the map and target node information of at least one of the plurality of nodes; and
and determining a target route of the target vehicle based on the target map, the departure location, and the destination. The method of claim 28, wherein determining a target route of the target vehicle based on the target map, the departure location, and the destination comprises:
determining a departure node from the plurality of nodes based on a departure location in the target map;
determining a destination node from the plurality of nodes based on the destination in the target map; and
determining a target route of the target vehicle based on a plurality of costs associated with a plurality of target nodes between the departure node and the destination node, wherein the cost of the node is a first cost from the departure node to the node. and determining a target route of a target vehicle that is determined based on a cost and a second cost from the node to the destination node. 30. The method of claim 29, wherein the first cost from the starting node to the node is the weight value of the node, the cost of the parent node of the node, the cost of the node from the parent node of the node to the node, and the node in the target map. is determined based on a cost of a turn from a parent node of 31. The method of any one of claims 26 to 30, further comprising: determining a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with the one or more vehicles, and the map. The steps are:
identifying one or more stationary vehicles from the one or more vehicles based on the motion state information;
determining, for each of the one or more stationary vehicles, a preset area comprising one or more nodes associated with the stationary vehicle;
for each of the one or more nodes associated with the stopped vehicle, modifying node information of the node;
determining a candidate map based on the map and modified node information of one or more nodes associated with each of the one or more stationary vehicles; and
and determining a candidate route of the target vehicle based on the candidate map, the departure location, and the destination. 32. The method of claim 31, wherein, for each of the one or more nodes associated with the stationary vehicle, modifying the node's node information comprises:
assigning a weight value greater than a predetermined threshold to the node; and
and designating the node as a non-turnable node. 33. The method of claim 31 or 32,
determining whether the candidate path at least partially overlaps with at least a portion of at least one predetermined path not traversed, wherein the at least one predetermined path is directed to at least one moving vehicle of the one or more vehicles. corresponding, determining whether the overlapping; and
in response to determining that the candidate route does not overlap with at least one untraveled predetermined route, designating the candidate route as the target route of the target vehicle. 34. The method of claim 33,
in response to determining that the candidate path at least partially overlaps with at least a portion of at least one predetermined path not traversed, modifying node information of the node for each of the one or more nodes associated with the stationary vehicle;
identifying one or more moving vehicles from the one or more vehicles based on the movement state information;
determining, for each of the one or more moving vehicles, one or more nodes associated with a predetermined path corresponding to the moving vehicle;
for each of the one or more nodes associated with the moving vehicle, modifying node information of the node;
determine a second candidate map based on the map, modified node information of one or more nodes associated with each of the one or more stationary vehicles, and modified node information of one or more nodes associated with each of the one or more moving vehicles to do; and
and determining a second candidate route of the target vehicle based on the second candidate map, the departure location, and the destination. 35. The method of claim 34,
For each of the one or more nodes associated with the stationary vehicle, modifying node information of the node comprises:
designating the node as a barrier;
For each of the one or more nodes associated with the moving vehicle, modifying node information of the node comprises:
and assigning to the node a weight value greater than a predetermined threshold. 36. The method of any one of claims 26 to 35, further comprising determining a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with the one or more vehicles, and the map. The steps are:
identifying one or more stationary vehicles from the one or more vehicles based on the motion state information;
determining, for each of the one or more stationary vehicles, a preset area comprising one or more nodes associated with the stationary vehicle;
for each of the one or more nodes associated with the stationary vehicle, modifying node information of the node by designating the node as a barrier;
identifying one or more moving vehicles from the one or more vehicles based on the movement state information;
determining, for each of the one or more moving vehicles, one or more nodes associated with a predetermined path corresponding to the moving vehicle;
modifying, for each of the one or more nodes associated with the moving vehicle, the node information of the node by assigning to the node a weight value greater than a predetermined threshold; determining the target map based on the map, modified node information of one or more nodes associated with each of the one or more stationary vehicles, and modified node information of one or more nodes associated with each of the one or more moving vehicles step; and
and determining a target route of the target vehicle based on the target map, the departure location, and the destination. As a system for route planning:
a location obtaining module configured to obtain a starting location and a destination of the target vehicle;
a map acquiring module configured to acquire a map of a target area including the departure location and the destination, wherein the map includes node information of each of a plurality of nodes;
a motion status obtaining module configured to obtain motion status information associated with one or more vehicles different from the target vehicle in the target area; and
and a route determination module configured to determine a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with the one or more vehicles, and the map. 38. The method of claim 37, wherein the node information of each of the plurality of nodes includes the location of the node, the barcode of the node, the coordinates of the node, the traversability of the node, the turning ability of the node, the number of adjacent nodes, or the A system for path planning, comprising at least one of a node's connectivity to the adjacent nodes. 39. The route determination of claim 37 or 38, to determine a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with one or more vehicles, and the map. The module is also:
determine, for at least one of the plurality of nodes, target node information by modifying at least a portion of each node information of at least one of the plurality of nodes based on motion state information associated with the one or more vehicles;
generate a target map based on the map and target node information of at least one of the plurality of nodes;
and determine a target route of the target vehicle based on the target map, the departure location, and the destination. 40. The method of claim 39, wherein to determine a target route of the target vehicle based on the target map, the departure location, and the destination, the route determination module further comprises:
determine a departure node from the plurality of nodes based on a departure location in the target map;
determine a destination node from the plurality of nodes based on a destination in the target map;
determine a target route of the target vehicle based on a plurality of costs associated with a plurality of target nodes between the departure node and the destination node, the cost of the node being a first cost from the departure node to the node and determined based on a second cost from the node to the destination node, configured. 41. The method of claim 40, wherein the first cost from the starting node to the node is the weight value of the node, the cost of the parent node of the node, the cost of the node from the parent node of the node to the node, and the node in the target map. A system for path planning, determined based on a turn cost from a parent node of 42. The method of any one of claims 37 to 41 for determining a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with one or more vehicles, and the map. , the path determination module also includes:
identify one or more stationary vehicles from the one or more vehicles based on the motion state information;
determine, for each of the one or more stationary vehicles, a preset area comprising one or more nodes associated with the stationary vehicle;
for each of the one or more nodes associated with the stationary vehicle, modify node information of the node;
determine a candidate map based on the map and modified node information of one or more nodes associated with each of the one or more stationary vehicles;
and determine a candidate route of the target vehicle based on the candidate map, the departure location, and the destination. 43. The method of claim 42, wherein, for each of the one or more nodes associated with the stopped vehicle, to modify the node's node information, the route determination module further comprises:
assign a weight value greater than a predetermined threshold to the node;
and designate the node as a non-turnable node. 44. The method of claim 42 or 43, wherein the route determination module further comprises:
determine whether the candidate path at least partially overlaps with at least a portion of at least one predetermined path not traversed, wherein the at least one predetermined path corresponds to a moving vehicle of at least one of the one or more vehicles; ;
and in response to determining that the candidate route does not overlap with the at least one untraveled predetermined route, designate the candidate route as the target route of the target vehicle. 45. The method of claim 44, wherein the path determination module further comprises:
in response to determining that the candidate path at least partially overlaps with at least a portion of at least one predetermined path not traversed, modify node information of the node for each of the one or more nodes associated with the stationary vehicle;
identify one or more moving vehicles from the one or more vehicles based on the movement state information;
determine, for each of the one or more moving vehicles, one or more nodes associated with a predetermined path corresponding to the moving vehicle;
for each of the one or more nodes associated with the moving vehicle, modify node information of the node;
determine a second candidate map based on the map, modified node information of one or more nodes associated with each of the one or more stationary vehicles, and modified node information of one or more nodes associated with each of the one or more moving vehicles do;
and determine a second candidate route of the target vehicle based on the second candidate map, the departure location, and the destination. 46. The method of claim 45,
For each of the one or more nodes associated with the stationary vehicle, to modify the node's node information, the route determination module further comprises:
configured to designate the node as a barrier;
For each of the one or more nodes associated with the moving vehicle, to modify node information of the node, the at least one processor causes the system to:
and assign a weight value greater than a predetermined threshold to the node. 47. The method of any of claims 37-46, further configured to determine a target route of the target vehicle based at least in part on the departure location, the destination, motion state information associated with the one or more vehicles, and the map. In order to do so, the path determination module may also:
identify one or more stationary vehicles from the one or more vehicles based on the motion state information;
determine, for each of the one or more stationary vehicles, a preset area comprising one or more nodes associated with the stationary vehicle;
for each of the one or more nodes associated with the stationary vehicle, modify node information of the node by designating the node as a barrier;
identify one or more moving vehicles from the one or more vehicles based on the movement state information;
determine, for each of the one or more moving vehicles, one or more nodes associated with a predetermined path corresponding to the moving vehicle;
modify, for each of the one or more nodes associated with the moving vehicle, the node information of the node by assigning to the node a weight value greater than a predetermined threshold; determine a target map based on the map, modified node information of one or more nodes associated with each of the one or more stationary vehicles, and modified node information of one or more nodes associated with each of the one or more moving vehicles;
and determine a target route of the target vehicle based on the target map, the departure location, and the destination. A non-transitory computer-readable medium comprising executable instructions, the executable instructions, when executed by at least one processor, instructing the at least one processor to perform a method, the method comprising:
obtaining a starting position and a destination of the target vehicle;
obtaining a map of a target area including the starting location and the destination, wherein the map includes node information of each of a plurality of nodes; obtaining a map of the target area;
acquiring motion state information associated with one or more vehicles different from the target vehicle in the target area; and
determining a target route of the target vehicle based at least in part on the starting location, the destination, motion state information associated with the one or more vehicles, and the map.

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